{"id":"W2615862631","doi":"10.1007/s00484-017-1353-x","title":"Investigation of the scaling characteristics of LANDSAT temperature and vegetation data: a wavelet-based approach","year":2017,"lang":"en","type":"article","venue":"International Journal of Biometeorology","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vegetation (pathology); Remote sensing; Wavelet; Environmental science; Geography; Scaling; Physical geography; Computer science; Artificial intelligence; Mathematics; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008926551,0.0003487962,0.0002644811,0.001280907,0.0001644465,0.0005582351,0.0002300731,0.0003505336,0.0004922381],"category_scores_gemma":[0.002642154,0.0001514729,0.0003788847,0.001306431,0.0002253336,0.0007642771,0.0002384114,0.0004389219,0.0001488521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001168549,"about_ca_system_score_gemma":0.0002073466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005898747,"about_ca_topic_score_gemma":0.0005082156,"domain_scores_codex":[0.9998239,0.00004612777,0.00001276497,0.00003194824,0.00006354357,0.0000218103],"domain_scores_gemma":[0.998881,0.0005864006,0.0001206148,0.00008967293,0.0002648663,0.00005748379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007678941,0.0005979644,0.05650026,0.0005632473,0.0002655821,0.001073456,0.0005960509,0.1227175,0.305132,0.01467961,0.001463638,0.4956428],"study_design_scores_gemma":[0.00001618247,0.0001546003,0.06747244,0.00002360951,0.000115267,0.0002508922,0.0002081704,0.9190391,0.009787145,0.001976168,0.0009287843,0.00002771407],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7818321,0.0005041884,0.2150149,0.000134354,0.00005776958,0.0000430936,0.0002296438,0.0001010971,0.002083005],"genre_scores_gemma":[0.9656791,0.0005621804,0.03293533,0.00001305597,0.00004431762,0.00002012911,0.0002959803,0.00004092289,0.0004090307],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001280907,"threshold_uncertainty_score":0.004720867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02409767014539739,"score_gpt":0.257414560200968,"score_spread":0.2333168900555706,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}